MIRARI

Head of Claude Code: "Fable 5 is our most powerful model for running self-improving agent systems. Add /loops, dynamic workflows, dreaming - and you are unstoppable." Most people are still prompting one message at a time One chat One answer Start over That is not how the people building Claude work The real setup is different Self-improving agent loops Dynamic workflows that run without you Dreaming that compounds every session In 12 minutes the Head of Claude shows you how to build this from scratch No $500 course No experience needed No gatekeeping Worth more than anything you will find behind a paywall this year Watch the video Bookmark the article before it gets buried in your feed

🤖 Hermes Agent: One of the Most Interesting Open-Source AI Agents Right Now Most AI agents can use tools. Hermes Agent is trying to do something harder: Learn from experience and improve over time. The architecture looks like: User ↓ Agent ↓ Tools ↓ Actions ↓ Memory ↓ Skill Creation ↓ Future Improvement What makes Hermes different? 1. Persistent Memory Most agents forget everything when the session ends. Hermes maintains memory across sessions and projects, allowing it to accumulate context and knowledge over time. 2. Self-Improving Skills When Hermes solves a difficult problem, it can create reusable "skills" that can be searched and reused later. Instead of solving the same problem repeatedly, it attempts to build a growing library of capabilities. 3. Multi-Platform Access One agent can operate through: • CLI • Telegram • Discord • Slack • WhatsApp • Signal The idea is: One memory. One agent. Many interfaces. 4. Multiple Execution Environments Tasks can run: • Locally • Inside Docker • Via SSH • On cloud infrastructure Making it useful for both personal and infrastructure automation. 5. Model Agnostic Design Hermes isn't tied to a single model provider. You can use: • OpenAI-compatible APIs • OpenRouter • Local models • Self-hosted inference endpoints This reduces vendor lock-in. The emerging AI stack looks like: LLM ↓ Tools ↓ Memory ↓ Planning ↓ Learning Loop ↓ Autonomous Agent The most important innovation may not be larger models. It may be agents that continuously accumulate knowledge, skills, and context.

Anthropic Managed Agents team: "Fable 5 is our best model for running self-improving agent systems. Add /loops, dynamic workflows, dreaming and you are unstoppable" in this video he breaks down exactly how most people are using Claude: - the 34% you lose to CLAUDE.md before typing a word - one agent researching. one building. one reviewing. one orchestrating - the architecture that separates hobbyists from real builders - the 3 properties every agent team needs to actually survive if you've been using Claude for more than a month and never left the chat window, you've been using one agent when you could be running a team of them instead of another show tonight, watch this make sure to bookmark it before it gets lost in your feed the guide is in the article below

i don't think the next breakthrough in agents is a bigger model. it's giving agents a way to learn from their own production mistakes. reading traces, identifying recurring behaviors, generating evals, and improving from real-world usage feels like a much more important direction than most people realize. this is basically that, shipped.

Next on MIRARI: collapsing the Mirror's reflection loop into a CRDT-backed signal log so live drift events, judge scores, and prompt diffs converge across tabs/devices without a server round-trip. Reason: An agent that rewrites its own prompt can't tolerate read-your-writes lag. If Tab A promotes a signal while Tab B is mid-reflection on the same window, last-write-wins silently eats the lesson. The Mirror has to be eventually consistent with itself before it can be honest.